PlotPolarGAMM_demo

PlotPolarGAMM is a function that allows you to visualize and plot Plotly figures of GAMM-fitted ultrasound tongue contours with provided GAMM models.

plotPolarGAMM(
model,
targetName,
targetL,
rmax,
view='X',
showLegend=T,
showDiff=T,
fontSize=25,
returnData=F
)

Arguments

model

A GAMM model, resulting from the functions gam or bam.

target

The name of the categories of different types of sounds (e.g. vowel for tokens of /i/, /a/, and /u/, or place for alveolar and velar sounds.)

targetL

The categories of the target (e.g. use c(‘i’, ‘a’, ‘u’) for the vowel example previously, or c(‘velar’, ‘alvolar’) for the place example.)

rmax

The maximum radius of the plot.

view

The predictor, usually ‘X’.

showLegend

Whether to show the legend.

showDiff

Whether to plot the different areas, indicated by gray color blocks.

fontSize

The font size.

returnData

Whether to return the fitted data as a dataframe. If False, it will return a Plotly plot.

Import plot_polar_GAMM and the other two modules used to perform polar GAMM.

source('plot_polar_GAMM.R')
library(itsadug)

## Loading required package: mgcv

## Loading required package: nlme

## This is mgcv 1.8-38. For overview type 'help("mgcv-package")'.

## Loading required package: plotfunctions

## Loaded package itsadug 2.4 (see 'help("itsadug")' ).

library(rticulate)

Import the ultrasound contour data. In contours_demo.csv, there are the tongue contour tracings of /i/, /u/, and /y/ at the mid time point.

df <- read.csv('contours_demo.csv')
df$vowel <- factor(df$vowel)
df$token <- factor(df$token)
df$X <- df$x
df$Y <- df$y
df <- start_event(df, event=c('token', 'vowel'), column="time_point")

Construct teh GAMM model.

model <- polar_gam(
  Y~ vowel +
    s(X, by=vowel) +
    s(token, bs='re'),
  data=df,
  origin=c(562, 573)
)

model.Acf <- acf_resid(model)

model <- polar_gam(
  Y ~ vowel +
    s(X, by=vowel) +
    s(token, bs='re'),
  data=df,
  origin=c(562, 573),
  rho=model.Acf[2],
  AR.start=df$start.event
)

Put the model into the function, and you will get the plot.

plotPolarGAM(model=model,
             targetName='vowel',
             targetL=c('i', 'u', 'y'),
             rmax=max(df$Y)*1.05,
             view='X',
             showDiff=F,
             returnData=F)

## Loading required package: gss

## Loading required package: ggplot2

## 
## Attaching package: 'ggplot2'

## The following object is masked from 'package:plotfunctions':
## 
##     alpha

## Loading required package: plotly

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:plotfunctions':
## 
##     add_bars

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

## Loading required package: sets

## Registered S3 method overwritten by 'sets':
##   method        from   
##   print.element ggplot2

## 
## Attaching package: 'sets'

## The following object is masked from 'package:plotly':
## 
##     %>%

## Loading required package: RColorBrewer

## Summary:
##  * vowel : factor; set to the value(s): i. 
##  * X : numeric predictor; with 30 values ranging from 1.038784 to 2.318545. 
##  * token : factor; set to the value(s): 0. (Might be canceled as random effect, check below.) 
##  * NOTE : The following random effects columns are canceled: s(token)
## 

## Warning in plotL[i] <- plot_smooth(model, view = view, cond =
## changeName(list(c(targetL[i])), : number of items to replace is not a multiple
## of replacement length

## Summary:
##  * vowel : factor; set to the value(s): u. 
##  * X : numeric predictor; with 30 values ranging from 1.038784 to 2.318545. 
##  * token : factor; set to the value(s): 0. (Might be canceled as random effect, check below.) 
##  * NOTE : The following random effects columns are canceled: s(token)
## 

## Warning in plotL[i] <- plot_smooth(model, view = view, cond =
## changeName(list(c(targetL[i])), : number of items to replace is not a multiple
## of replacement length

## Summary:
##  * vowel : factor; set to the value(s): y. 
##  * X : numeric predictor; with 30 values ranging from 1.038784 to 2.318545. 
##  * token : factor; set to the value(s): 0. (Might be canceled as random effect, check below.) 
##  * NOTE : The following random effects columns are canceled: s(token)
## 

## Warning in plotL[i] <- plot_smooth(model, view = view, cond =
## changeName(list(c(targetL[i])), : number of items to replace is not a multiple
## of replacement length

You can also set returnData to True, and get the estimated contours.

df <- plotPolarGAM(model=model,
                   targetName='vowel',
                   targetL=c('i', 'u', 'y'),
                   rmax=max(df$Y)*1.05,
                   view='X',
                   showDiff=F,
                   returnData=T)

## Summary:
##  * vowel : factor; set to the value(s): i. 
##  * X : numeric predictor; with 30 values ranging from 1.038784 to 2.318545. 
##  * token : factor; set to the value(s): 0. (Might be canceled as random effect, check below.) 
##  * NOTE : The following random effects columns are canceled: s(token)
## 

## Warning in plotL[i] <- plot_smooth(model, view = view, cond =
## changeName(list(c(targetL[i])), : number of items to replace is not a multiple
## of replacement length

## Summary:
##  * vowel : factor; set to the value(s): u. 
##  * X : numeric predictor; with 30 values ranging from 1.038784 to 2.318545. 
##  * token : factor; set to the value(s): 0. (Might be canceled as random effect, check below.) 
##  * NOTE : The following random effects columns are canceled: s(token)
## 

## Warning in plotL[i] <- plot_smooth(model, view = view, cond =
## changeName(list(c(targetL[i])), : number of items to replace is not a multiple
## of replacement length

## Summary:
##  * vowel : factor; set to the value(s): y. 
##  * X : numeric predictor; with 30 values ranging from 1.038784 to 2.318545. 
##  * token : factor; set to the value(s): 0. (Might be canceled as random effect, check below.) 
##  * NOTE : The following random effects columns are canceled: s(token)
## 

## Warning in plotL[i] <- plot_smooth(model, view = view, cond =
## changeName(list(c(targetL[i])), : number of items to replace is not a multiple
## of replacement length

write.csv(df, 'results.csv')